Adaboost is a boosting algorithm which combines weak learners into a strong classifier. Let’s learn building Adaboost classifier

# Imports
from sklearn.datasets import load_iris
from sklearn.ensemble import AdaBoostClassifier
import pandas as pd
import numpy as np

# Load Data
iris = load_iris()

# Create a dataframe
df = pd.DataFrame(iris.data, columns = iris.feature_names)
df['target'] = iris.target

# Let's see a sample of created df
df.sample(frac=0.01)
 sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
465.13.81.60.20
695.62.53.91.11
# Let's see target names
targets = iris.target_names
print(targets)
 
['setosa' 'versicolor' 'virginica']
# Prepare training data for building the model
X_train = df.drop(['target'], axis=1).values
y_train = df['target']

# Instantiate the model
cls = AdaBoostClassifier()

# Train/Fit the model 
cls.fit(X_train, y_train)

# Make prediction using the model
X_pred = [5.1, 3.2, 1.5, 0.5]
y_pred = cls.predict([X_pred])

print("Prediction is: {}".format(targets[y_pred]))
 
Prediction is: ['setosa']
 

That's how we Build Adaboost classifier model

That’s all for this mini tutorial. To sum it up, we learned how to Build Adaboost classifier model.

Hope it was easy, cool and simple to follow. Now it’s on you.

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